An intelligent decision support approach for quantified assessment of innovation ability via an improved BP neural network

In today's competitive and changing social environment, innovation and entrepreneurial ability have become important factors for the successful development of college students. However, relying solely on traditional evaluation methods and indicators cannot comprehensively and accurately evaluate the innovation and entrepreneurial potential and ability of college students. Therefore, developing a comprehensive evaluation model is urgently needed. To address this issue, this article introduces machine learning methods to explore the learning ability of subjective evaluation processes and proposes an intelligent decision support method for quantitatively evaluating innovation capabilities using an improved BP (Back Propagation) neural network. This article first introduces the current research status of evaluating the innovation and entrepreneurship ability of college students, and based on previous research, it has been found that inconsistent evaluation standards are one of the important issues at present. Then, based on different BP models and combined with the actual situation of college student innovation and entrepreneurship evaluation, we selected an appropriate input layer setting for the BP neural network and improved the setting of the middle layer (hidden layer). The identification of output nodes was also optimized by combining the current situation. Subsequently, the conversion function, initial value and threshold were determined. Finally, evaluation indicators were determined and an improved BP model was established which was validated using examples. The research results indicate that the improved BP neural network model has a low error rate, strong generalization ability and ideal prediction effect which can be effectively used to analyze problems related to intelligent evaluation of innovation ability

Global Existence and Blow-Up for a Chemotaxis System

In this paper we consider a Keller-Segel-type chemotaxis model with reaction term under no-flux boundary conditions, where the kinetics term of the system is power function. Assuming some growth conditions, the existence of bounded global strong solution to the parabolic-parabolic system is given. We also give the numerical test and find out that there exists a threshold. When the power frequency greater than the threshold, both global solution and blow-up solution exist

Homologous haplotypes, expression, genetic effects and geographic distribution of the wheat yield gene TaGW2

BACKGROUND: TaGW2-6A, cloned in earlier research, strongly influences wheat grain width and TKW. Here, we mainly analyzed haplotypes of TaGW2-6B and their effects on TKW and interaction with haplotypes at TaGW2-6A. RESULTS: About 2.9 kb of the promoter sequences of TaGW2-6B and TaGW2-6D were cloned in 34 bread wheat cultivars. Eleven SNPs were detected in the promoter region of TaGW2-6B, forming 4 haplotypes, but no divergence was detected in the TaGW2-6D promoter or coding region. Three molecular markers including CAPS, dCAPS and ACAS, were developed to distinguish the TaGW2-6B haplotypes. Haplotype association analysis indicated that TaGW2-6B has a stronger influence than TaGW2-6A on TKW, and Hap-6B-1 was a favored haplotype increasing grain width and weight that had undergone strong positive selection in global wheat breeding. However, clear geographic distribution differences for TaGW2-6A haplotypes were found; Hap-6A-A was favored in Chinese, Australian and Russian cultivars, whereas Hap-6A-G was preferred in European, American and CIMMYT cultivars. This difference might be caused by a flowering and maturity time difference between the two haplotypes. Hap-6A-A is the earlier type. Haplotype interaction analysis between TaGW2-6A and TaGW2-6B showed additive effects between the favored haplotypes. Hap-6A-A/Hap-6B-1 was the best combination to increase TKW. Relative expression analysis of the three TaGW2 homoeologous genes in 22 cultivars revealed that TaGW2-6A underwent the highest expression. TaGW2-6D was the least expressed during grain development and TaGW2-6B was intermediate. Diversity of the three genes was negatively correlated with their effect on TKW. CONCLUSIONS: Genetic effects, expression patterns and historic changes of haplotypes at three homoeologous genes of TaGW2 influencing yield were dissected in wheat cultivars. Strong and constant selection to favored haplotypes has been found in global wheat breeding during the past century. This research also provides a valuable case for understanding interaction of genes that control complex traits in polyploid species

The Effect of Different Laser Irradiation on Cyclophosphamide-Induced Leucopenia in Rats

Objective. To assess the effect of different lasers on cyclophosphamide- (CTX-) induced leucopenia in rats. Methods. 11 rats were normal control and 55 rats were injected with a dose of 80 mg/kg CTX for the first time and 40 mg/kg on the 6th and the 11th days to establish a leucopenia model. Rats of the irradiation groups received a 5-minute laser irradiation with either single 10.6 μm or 650 nm laser or alternatively 10.6 μm–650 nm laser irradiation, besides a sham treatment on acupoint Dazhui (DU 14) and acupoint Zusanli (ST 36) of both sides, 8 times for 16 days. Normal and model control group received no treatment. Results. On day 16 after the first CTX injection, the WBC counts from all the laser irradiation groups were significantly higher than those from the model control and the sham group (P<0.05), while there were no significant differences compared with the normal control (P>0.05). The TI of 10.6 μm–650 nm laser irradiation group was significantly higher than that of the model control group (P<0.05). Conclusions. The single and combined 10.6 μm and 650 nm laser irradiation on ST36 and DU14 accelerated the recovery of the WBC count in the rats with leucopenia